Publishready

PublishReady: Professional Writing Control

PublishReady is a deterministic writing analysis system designed to turn AI drafts into publish-ready writing. It serves as the final QA pass for AI-generated prose, providing local-first metrics, target compliance, and specific revision levers without sending text to remote services.

The PublishReady Packages

This project is structured as a professional, layered monorepo containing specialized packages:

Core Packages

• [@veldica/publishready-mcp](packages/mcp): The Model Context Protocol (MCP) server implementation (publishready-mcp).
• [@veldica/publishready-cli](packages/cli): The command-line tool for local analysis (publishready).
• [@veldica/publishready-core](packages/core): The central orchestration engine.
• [@veldica/publishready-schemas](packages/schemas): Unified Zod schemas and explicit interfaces.

Underlying Libraries

• [@veldica/prose-analyzer](https://www.npmjs.com/package/@veldica/prose-analyzer): Deterministic style signals (variety, density, repetition, narrative texture).
• [@veldica/readability](https://www.npmjs.com/package/@veldica/readability): Consolidated library of all major readability formulas.
• [@veldica/prose-tokenizer](https://www.npmjs.com/package/@veldica/prose-tokenizer): Standalone markdown-aware prose tokenization.
• [@veldica/prose-linter](https://www.npmjs.com/package/@veldica/prose-linter): Target checks, revision levers, content integrity, and deterministic AI-sounding prose markers.

Installation

MCP Server (Recommended)

Add the server to your MCP client configuration:

{

"mcpServers": { "publishready": { "command": "npx", "args": ["-y", "@veldica/publishready-mcp"] } } }

Command Line

npx @veldica/publishready-cli analyze sample.txt

Hosted MCP

For Smithery, VPS, or gateway deployments, run the server with Streamable HTTP:

npx @veldica/publishready-mcp --transport=http --port=3000

The MCP endpoint is /mcp; the health endpoint is /health.

Key Features

• Template, Target, and Reference Modes: Compare writing against built-in templates, explicit numeric targets, reference text, or reusable reference profiles.
• Deterministic Metrics: Structural counts, sentence and paragraph distributions, lexical signals, scannability, fiction proxies, and readability formulas.
• Specific Revision Levers: Ranked, evidence-based suggestions such as shorten_long_sentences, replace_difficult_words, and reduce_abstract_wording.
• AI-Sounding Prose Audit: Deterministic marker inventory for formulaic, generic, or over-polished prose, including exact matches and tracked phrase counts.
• Fiction & Non-Fiction Support: Narrative metrics for dialogue, sensory density, abstract wording, and scene pacing.
• Explainable Interpretation: Target and metric interpretation that explains audience, use cases, style implications, and tradeoffs.
• Local-First & Private: Stdio-first, deterministic, no external API calls, and no LLM wrappers.

MCP Tool Surface

The MCP server exposes 16 specialized tools for analysis and control, including audit_ai_sounding_prose for deterministic AI-marker analysis. For a full list and documentation, see the MCP README.

Deterministic Philosophy

This package explicitly avoids perplexity and other model-dependent scores. We believe writing control should be:

1. Explainable: You should know exactly why a score changed.
2. Reproducible: The same text should always yield the same metrics.
3. Practical: A metric is only useful if it tells you what to change.

Development

npm install

npm run build npm run lint npm run typecheck npm test

Publishing Metadata

• npm package: @veldica/publishready-mcp
• MCP Registry name: io.github.veldica/publishready
• Product homepage: https://veldica.com/publish-ready
• Source repository: https://github.com/veldica/publishready-mcp

Registry and Directory Metadata

PublishReady is prepared for MCP directory discovery through:

• GitHub repository topics
• npm package keywords
• Glama metadata via glama.json
• Official MCP Registry metadata via mcpName

This MCP is designed for AI-assisted writing workflows where the model should improve clarity, structure, readability, and publish-readiness while preserving facts, terminology, intent, and author voice.

License

MIT